{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import transformers\n",
    "from transformers import AutoTokenizer, AutoModelWithLMHead\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ADMIN\\textifyai\\lib\\site-packages\\transformers\\models\\auto\\modeling_auto.py:664: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "# model= AutoModelWithLMHead.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Can't get attribute 'NewGELUActivation' on <module 'transformers.activations' from 'c:\\\\Users\\\\ADMIN\\\\textifyai\\\\lib\\\\site-packages\\\\transformers\\\\activations.py'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9168/1093822913.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Gpt_neo_Epoch_10_Loss_031_data_5000.pth\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cpu'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\ADMIN\\textifyai\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[0;32m    605\u001b[0m                     \u001b[0mopened_file\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig_position\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    606\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 607\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    608\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ADMIN\\textifyai\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36m_load\u001b[1;34m(zip_file, map_location, pickle_module, pickle_file, **pickle_load_args)\u001b[0m\n\u001b[0;32m    880\u001b[0m     \u001b[0munpickler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mUnpicklerWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_file\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    881\u001b[0m     \u001b[0munpickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpersistent_load\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpersistent_load\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 882\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munpickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_loaded_sparse_tensors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\ADMIN\\textifyai\\lib\\site-packages\\torch\\serialization.py\u001b[0m in \u001b[0;36mfind_class\u001b[1;34m(self, mod_name, name)\u001b[0m\n\u001b[0;32m    873\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mfind_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmod_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    874\u001b[0m             \u001b[0mmod_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_module_mapping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmod_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmod_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 875\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmod_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    877\u001b[0m     \u001b[1;31m# Load the data (which may in turn use `persistent_load` to load tensors)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Can't get attribute 'NewGELUActivation' on <module 'transformers.activations' from 'c:\\\\Users\\\\ADMIN\\\\textifyai\\\\lib\\\\site-packages\\\\transformers\\\\activations.py'>"
     ]
    }
   ],
   "source": [
    "model=torch.load(\"Gpt_neo_Epoch_10_Loss_031_data_5000.pth\",map_location=torch.device('cpu'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_query(input_sentence,max_len=40,temp=0.7):\n",
    "    pred=[]\n",
    "    seq=tokenizer(input_sentence,return_tensors='pt')['input_ids'].to(device)\n",
    "    outputs=model.generate(seq,\n",
    "                          max_length=max_len,\n",
    "                          do_sample=True,\n",
    "                          top_p=0.95,\n",
    "                          #num_beams=5,\n",
    "                          temperature=temp,\n",
    "                          no_repeat_ngram_size=3,\n",
    "                          num_return_sequences=5\n",
    "                          ).to(device)\n",
    "    for i,out in enumerate(outputs):\n",
    "      out=tokenizer.decode(out, skip_special_tokens=True)\n",
    "      idx=out.find(\"<|sep|>\")+7\n",
    "      out=out[idx:]\n",
    "      print(f\"Sugestion{i} :{out}\")\n",
    "      pred.append(tokenizer.decode(out, skip_special_tokens=True))\n",
    "    return pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.11.3\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "\n",
    "print(transformers.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.2+cu111\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3544/763479854.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tf.__version__\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "print(\"tf.__version__\", tf.__version__)"
   ]
  }
 ],
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  "interpreter": {
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